A display method and system for a smart wearable device

Abstract

The present invention discloses a display method and system for an intelligent wearable device. The method specifically includes: real-time detecting the light intensity data and color temperature data of the usage environment of the intelligent wearable device, and dynamically adjusting the first display parameters of the intelligent wearable device according to the light intensity data and the color temperature data; obtaining the historical operation information of the user on the interface elements of the intelligent wearable device, and performing personalized adjustment on the layout of the interface elements of the intelligent wearable device according to the historical operation information; according to the characteristics and importance of the display content of the intelligent wearable device, dynamically adjusting the second display parameters of each display area of the display content by using an adaptive display algorithm, where the second display parameters include resolution and refresh rate. The present invention significantly improves the matching degree between the intelligent wearable device and the user's visual perception, reduces visual fatigue, optimizes the information transmission efficiency, and provides a more natural and efficient visual experience for the user.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent wearable devices, and particularly to a display method and system for intelligent wearable devices. Background Art

[0002] In the fields of intelligent wearable devices, virtual reality (VR) and augmented reality (AR) devices, the presentation mode of display content is closely related to the user's visual experience. However, the existing display methods face multiple technical challenges, which affect the overall user experience.

[0003] First of all, the matching degree between the display content and the user's line of sight is an urgent problem to be solved. When the user makes head movements or eye rotations, if the display content cannot be adjusted in real time, it will lead to visual incoherence and unnaturalness. Especially in fast-moving scenarios, the delay or misalignment of the display content will significantly reduce the user's immersion and comfort. The root cause of this problem is that the existing display methods lack the ability to respond to environmental changes and user behaviors in real time.

[0004] Secondly, the fixity of the display area also limits the user experience. In some cases, the user may need to focus on a certain fixed area for a long time, but the existing display area size and shape often cannot meet this requirement, resulting in visual fatigue. And in scenarios where a large amount of information needs to be quickly browsed, the fixed display area limits the information display space and reduces the information acquisition efficiency. This is mainly because the current display methods fail to make flexible adjustments according to the specific needs and usage scenarios of the user.

[0005] In addition, the change of environmental light also has a significant impact on the display effect of VR / AR devices. In outdoor strong light or indoor dim light environments, if the brightness and contrast of the display screen cannot be adjusted adaptively, it will lead to reduced content visibility, impaired clarity, and even possible damage to the user's eyesight. The root cause of this problem is that the existing display methods lack the ability to effectively sense and dynamically adjust environmental light.

[0006] Finally, the personalized needs of different users are also a challenge faced by the current display methods. Different users have different preferences for the color, size, shape, etc. of the display area. If customization cannot be carried out according to the personalized needs of the user, the user satisfaction and usage experience will be reduced. This is mainly because the existing display methods lack an in-depth understanding and flexible support for the personalized needs of the user. Summary of the Invention

[0007] The object of the present invention is to provide a display method and system for intelligent wearable devices, which significantly improves the matching degree between intelligent wearable devices and users' visual perception, reduces visual fatigue, optimizes the information transmission efficiency, and provides users with a more natural and efficient visual experience, so as to solve at least one of the above-mentioned problems in the prior art.

[0008] In the first aspect, the present invention provides a display method for intelligent wearable devices, and the method specifically includes:

[0009] Real-time detection of the light intensity data and color temperature data of the usage environment of the intelligent wearable device, and dynamically adjusting the first display parameters of the intelligent wearable device according to the light intensity data and the color temperature data, where the first display parameters include display brightness, display contrast, and display color;

[0010] Obtaining the historical operation information of the user on the interface elements of the intelligent wearable device, and performing personalized adjustment on the layout of the interface elements of the intelligent wearable device according to the historical operation information;

[0011] According to the characteristics and importance of the display content of the intelligent wearable device, dynamically adjusting the second display parameters of each display area of the display content by using an adaptive display algorithm, where the second display parameters include resolution and refresh rate;

[0012] Determining whether to optimize the layout of the display content of the current interaction interface according to the user's eye fatigue degree and the residence time of the current interaction interface;

[0013] Obtaining the historical gaze information of the user when operating the intelligent wearable device, training the attention mechanism model according to the historical gaze information, and determining the loading priority of each display area of the intelligent wearable device through the trained attention mechanism model.

[0014] In the second aspect, the present invention provides a display system for intelligent wearable devices, and the system specifically includes:

[0015] The first display module is used for real-time detection of the light intensity data and color temperature data of the usage environment of the intelligent wearable device, and dynamically adjusting the first display parameters of the intelligent wearable device according to the light intensity data and the color temperature data, where the first display parameters include display brightness, display contrast, and display color;

[0016] The second display module is used for obtaining the historical operation information of the user on the interface elements of the intelligent wearable device, and performing personalized adjustment on the layout of the interface elements of the intelligent wearable device according to the historical operation information;

[0017] A third display module, configured to dynamically adjust second display parameters of each display area of the display content according to the characteristics and importance of the display content of the smart wearable device, where the second display parameters include resolution and refresh rate, by using an adaptive display algorithm;

[0018] A fourth display module, configured to determine whether to optimize the layout of the display content of the current interaction interface according to the user's eye fatigue degree and the residence time of the current interaction interface;

[0019] A fifth display module, configured to obtain historical fixation information when the user operates the smart wearable device, train an attention mechanism model according to the historical fixation information, and determine the loading priorities of the respective display areas of the smart wearable device through the trained attention mechanism model.

[0020] In a third aspect, the present invention provides a computer device, including: a memory, a processor, and a computer program stored on the memory, where when the computer program is executed on the processor, a display method of a smart wearable device as described in any one of the above methods is implemented.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, where when the computer program is run by a processor, a display method of a smart wearable device as described in any one of the above methods is implemented.

[0022] Compared with the prior art, the present invention has at least one of the following technical effects:

[0023] 1. The present invention significantly improves the matching degree between the smart wearable device and the user's visual perception, reduces visual fatigue, optimizes the information transmission efficiency, and provides a more natural and efficient visual experience for the user.

[0024] 2. The present invention can significantly improve the matching degree between the display content of the smart wearable device and the user's line of sight, enhance the flexibility of the display area of the smart wearable device, improve the adaptive ability of the display effect of the smart wearable device, and meet the personalized needs of the user, thereby providing a more high-quality and comfortable visual experience for the user.

[0025] 3. By detecting the light intensity and color temperature of the usage environment in real time, the present invention can automatically adjust the display brightness, contrast, and color of the smart wearable device to adapt to different lighting conditions, which can not only improve the readability of the display content, but also reduce eye fatigue and protect the user's eyesight.

[0026] 4. By analyzing the historical operation information of the user on the interface elements, the present invention can learn and understand the personalized usage habits of the user, thereby automatically adjusting the layout of the interface elements, improving the user experience, and enabling the user to access the required functions more conveniently.

[0027] 5. The present invention can identify the characteristics and importance of the display content, and dynamically adjust the resolution and refresh rate of the display area according to this information. It can not only improve the clarity of the display content, but also ensure that important information is preferentially displayed, thus enhancing the user's information acquisition efficiency.

[0028] 6. By monitoring the user's eye fatigue degree and the staying time on the interaction interface, the present invention can determine whether it is necessary to optimize the layout of the display content on the current interaction interface, reduce the user's visual burden, and improve the comfort and satisfaction of the user when using the smart wearable device.

[0029] 7. After determining that it is necessary to optimize the layout of the display content on the current interaction interface, the present invention can readjust the layout of the display content based on the user's historical interaction information on this interface, ensure that the display content more conforms to the user's usage habits and needs, and enhance the user experience.

[0030] 8. By analyzing the user's historical fixation information, the present invention can train an attention mechanism model, and determine the loading priority of each display area of the smart wearable device according to this model, ensure that the information that the user pays the most attention to is preferentially loaded and displayed, and improve the efficiency and accuracy of information presentation.

[0031] 9. After determining the loading priority of each display area, the present invention can dynamically adjust the information loading progress of each display area according to the network condition and device performance of the smart wearable device, ensure that the loading speed of the information matches the user's needs and the device's capabilities, and enhance the overall user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0033] Figure 1 is a schematic flowchart of a display method of a smart wearable device provided by an embodiment of the present invention;

[0034] Figure 2 is a schematic structural diagram of a display system of a smart wearable device provided by an embodiment of the present invention;

[0035] Figure 3 is a schematic structural diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] In the following description, specific details such as specific system architectures, technologies, etc. are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0037] It should be understood that when used in the specification and appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0038] It should also be understood that the term "and / or" used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0039] As used in the specification and appended claims of the present application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" depending on the context.

[0040] In addition, in the description of the specification and appended claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0041] The reference to "one embodiment" or "some embodiments" etc. described in the specification of the present application means that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the present application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0042] In the embodiments of the present application, the execution subject of the process includes a terminal device. The terminal device includes, but is not limited to, devices such as servers, computers, smart phones, and tablet computers that can execute the methods disclosed in the present application. Figure 1 The flowchart of the display method of the smart wearable device disclosed in an embodiment of the present invention is shown as follows and will be described in detail:

[0043] S101, real-time detect the light intensity data and color temperature data of the usage environment of the smart wearable device, and dynamically adjust the first display parameters of the smart wearable device according to the light intensity data and the color temperature data, where the first display parameters include display brightness, display contrast, and display color.

[0044] In this embodiment, the smart wearable device includes a light sensor, a color temperature sensor, a processor, and a display screen. The light sensor is used to real-time detect the light intensity data of the usage environment, and the color temperature sensor is used to real-time detect the color temperature data of the usage environment. The processor receives the data of the light sensor and the color temperature sensor, and dynamically adjusts the display brightness, display contrast, and display color of the display screen according to these data. Specifically, the light sensor periodically or real-time collects the light intensity data of the usage environment, the color temperature sensor periodically or real-time collects the color temperature data of the usage environment, the processor receives the data of the light sensor and the color temperature sensor, determines the optimal display brightness, display contrast, and display color of the display screen under different light intensity and color temperature conditions through machine learning or a preset algorithm based on the historical light intensity data and color temperature data, and then maps the current light intensity data and color temperature data to the optimal display parameters to obtain the optimal display parameters in the current environment. The processor sends the calculated optimal display parameters to the display screen. The display screen adjusts the display brightness, display contrast, and display color according to the received parameters.

[0045] In this embodiment, by real-time detecting the light intensity and color temperature of the usage environment and dynamically adjusting the display parameters of the display screen, the system can ensure that the display screen provides the best display effect in different environments. Users do not need to manually adjust the display parameters to obtain a clear and comfortable display effect, improving the user experience. In an environment with too strong or too weak light, the system can automatically adjust the display brightness to avoid damage to the user's eyes caused by the screen being too bright or too dark. By adjusting the display contrast and display color, the system can reduce screen flicker and reflection, further protecting the user's eyesight.

[0046] S102, obtain the historical operation information of the user on the interface elements of the smart wearable device, and perform personalized adjustment on the layout of the interface elements of the smart wearable device according to the historical operation information.

[0047] In this embodiment, the smart wearable device includes a data acquisition module, a data analysis module, and a layout adjustment module. The data acquisition module is responsible for collecting historical operation information of the user on the interface elements of the smart wearable device. The data analysis module analyzes this information to understand the user's usage habits and preferences. Finally, the layout adjustment module makes personalized adjustments to the layout of the interface elements according to the analysis results. Specifically, through the data acquisition module of the smart wearable device, historical operation information such as clicks, swipes, and long presses of the user on interface elements (such as buttons, icons, text boxes, etc.) is collected, and data such as the access frequency, stay time, and operation sequence of the user on each interface element is recorded. The data analysis module preprocesses the collected historical operation information, such as deduplication and noise reduction, and uses data mining and machine learning techniques to analyze the user's usage habits and preferences, such as which interface elements the user often accesses and which layout method the user prefers more. According to the analysis results, a probability density function of the user's personalized needs is generated to guide the layout adjustment. The layout adjustment module makes personalized adjustments to the layout of the interface elements of the smart wearable device according to the data analysis results. For example, if a user often accesses a specific function, the interface elements corresponding to this function can be placed in a more prominent position; if the user prefers a simple layout, unnecessary interface elements can be reduced or their arrangement can be optimized. The adjusted layout scheme needs to be tested and verified to ensure that it conforms to the user's usage habits and preferences and improves the user experience.

[0048] In this embodiment, by making personalized adjustments to the layout of the interface elements, the interface of the smart wearable device becomes more in line with the user's usage habits and preferences, thereby improving the user experience. The user can access the required functions more conveniently, reducing unnecessary operation steps and time costs. The personalized interface layout can increase the user's love and dependence on the smart wearable device, thereby enhancing user stickiness. The convenience and comfort felt by the user during use will prompt them to be more willing to continue using and recommend the device.

[0049] S103. According to the characteristics and importance of the display content of the smart wearable device, an adaptive display algorithm is used to dynamically adjust the second display parameters of each display area of the display content, where the second display parameters include resolution and refresh rate.

[0050] In this embodiment, first, the display content on the smart wearable device, including text, images, videos, etc., is monitored in real time through sensors. Then, technologies such as image processing and natural language processing are used to extract features and evaluate the importance of the display content. For example, key regions in an image (such as a face, text, etc.) can be identified and their importance to the user can be evaluated. According to the results of content monitoring and analysis, the system adopts an adaptive display algorithm to dynamically adjust the resolution and refresh rate of each display area. The core of the algorithm is to allocate appropriate display resources according to the characteristics and importance of the display content. For example, for important display content (such as emergency notifications, key information prompts, etc.), its resolution and refresh rate can be increased to ensure that the user can clearly see and quickly respond; while for secondary display content (such as background images, non-critical information, etc.), its resolution and refresh rate can be reduced to save energy and extend the device's battery life. When adjusting the resolution and refresh rate, the hardware limitations and energy management strategies of the device are also considered to ensure that the adjusted parameters meet the user's needs without affecting the performance and battery life of the device. In addition, the adaptive display algorithm will be continuously optimized according to the user's feedback and usage habits to improve its accuracy and adaptability.

[0051] In this embodiment, through the adaptive display algorithm, the system can dynamically adjust the resolution and refresh rate of each display area according to the characteristics and importance of the display content, thereby improving the display effect. The user can obtain a clearer and smoother visual experience in different usage scenarios. By reducing the resolution and refresh rate of secondary display content, the system can save energy and extend the battery life of the smart wearable device, which is particularly important for smart wearable devices that need to be worn and used for a long time.

[0052] S104. Determine whether to optimize the layout of the display content of the current interaction interface according to the user's eye fatigue degree and the residence time of the current interaction interface.

[0053] In this embodiment, the smart wearable device includes an eye movement tracking sensor, a user behavior analysis module, and a layout optimization module. The eye movement tracking sensor is used to monitor in real time parameters such as the user's eye movement trajectory and blink frequency, so as to evaluate the user's eye fatigue degree. When the eye fatigue degree reaches a preset threshold, it is considered that the user may be in an eye fatigue state. Through the user behavior analysis module, the residence time of the user on the current interaction interface is recorded. If the residence time of the user on a certain interface exceeds a preset time threshold and there is no obvious interaction operation (such as clicking, swiping, etc.), the system considers that the user may be confused or dissatisfied with the layout or content of the interface. When it is detected that the user has a high eye fatigue degree or a too long residence time on the current interaction interface, the built-in algorithm of the layout optimization module is triggered. The layout optimization module generates multiple possible layout optimization schemes according to factors such as the display content of the current interface, the user's usage habits, and the device hardware limitations. By comparing the expected effects (such as user satisfaction, operation efficiency, etc.) of different schemes, the optimal layout optimization scheme is selected for implementation.

[0054] In this embodiment, by monitoring the user's eye fatigue degree and residence time in real time, it is possible to timely discover the dissatisfaction and confusion of the user with the current interaction interface and automatically perform layout optimization, thereby improving the user experience. The layout-optimized interface is more in line with the user's visual habits and operation requirements, which helps to reduce the user's eye fatigue and visual burden.

[0055] S105, Obtain the historical fixation information when the user operates the smart wearable device, train the attention mechanism model according to the historical fixation information, and determine the loading priorities of the respective display areas of the smart wearable device through the trained attention mechanism model.

[0056] In this embodiment, the built-in eye movement tracking sensor of the smart wearable device is used to record key information such as the fixation point position and fixation duration of the user during device operation in real time. These information are summarized into a historical fixation information database to provide data support for subsequent analysis and model training. According to the characteristics of the historical fixation information, a deep learning model based on the attention mechanism is designed. This model can receive the user's historical fixation information as input and output the loading priorities of each display area. The input layer of the model includes features such as the position and fixation duration of the user's fixation point, and the output layer is the loading priority score corresponding to the display area. The attention mechanism model is trained using the data in the historical fixation information database. By adjusting the parameters and structure of the model, it can accurately predict the degree of attention of the user to each display area in different situations. During the training process, methods such as cross-validation are used to evaluate the model to ensure its generalization ability and accuracy. When the user uses the smart wearable device again, the loading priorities of each display area are calculated in real time through the trained attention mechanism model according to the current operation scenario and the user's historical fixation information. According to the calculated priorities, the loading order and display content of each display area are intelligently adjusted to optimize the user's visual experience and device performance. Finally, the user's feedback data and new historical fixation information are continuously collected for continuous optimization and update of the attention mechanism model. In this way, the smart wearable device can continuously adapt to the user's usage habits and changing needs, and provide a more personalized loading priority optimization solution.

[0057] In this embodiment, by intelligently determining the loading priorities of each display area, it can ensure that the information that the user is most concerned about is presented to the user first, thereby enhancing the user's visual experience and satisfaction. The intelligent adjustment of the loading priority helps to reduce the resource consumption and response time of the device, and improve the overall performance and operation efficiency of the device. It can continuously optimize and adjust the determination strategy of the loading priority according to the user's historical fixation information and feedback data to achieve a more personalized user experience.

[0058] In some embodiments, in the above step S101, the dynamically adjusting the first display parameter of the smart wearable device according to the light intensity data and the color temperature data specifically includes:

[0059] Obtain historical light intensity data and historical color temperature data, and determine the optimal display brightness, display contrast, and optimal display color of the smart wearable device under different light intensity conditions and different color temperature conditions according to the historical light intensity data and the historical color temperature data to form a first training data set;

[0060] Taking the first training data set as input, a support vector machine algorithm is used for modeling training to form a first display parameter mapping model;

[0061] Input the light intensity data and the color temperature data into the first display parameter mapping model for matching to obtain a matching result, and dynamically adjust the first display parameters of the smart wearable device according to the matching result.

[0062] In this embodiment, the ambient light intensity data and color temperature data of the smart wearable device over a period of time are obtained and stored as historical data. According to the adjustment of the user's display parameters under different light intensity and color temperature conditions in the historical data, the optimal display brightness, display contrast, and display color parameters under various conditions are determined to generate a training data set. The training data set is input into the support vector machine algorithm for modeling training to obtain a display parameter mapping model, which can match the optimal display parameter combination according to the input light intensity and color temperature data. The light intensity data and color temperature data of the current environment are collected in real time through the sensors on the smart wearable device and input into the trained display parameter mapping model. The display brightness, display contrast, and display color parameters that are most matched to the current light intensity and color temperature are obtained from the output result of the display parameter mapping model. Apply the optimal display parameters obtained in the previous step to the screen display settings of the smart wearable device to dynamically adjust its display brightness, contrast, and color. Monitor the changes in the ambient light intensity and color temperature. Once the changes exceed a certain threshold, repeat the above steps to dynamically optimize the screen display effect of the smart wearable device in real time to adapt to different environmental lighting conditions.

[0063] Exemplarily, first, historical light intensity and color temperature data are obtained. These data can be from the light sensor built into the smart wearable device. For example, under outdoor sunlight, the light intensity may reach 100,000 lux and the color temperature is about 5500K; while under indoor fluorescent lights, the light intensity may be 500 lux and the color temperature is about 4000K. By collecting these data over a long period, a comprehensive ambient light database can be established. Next, the optimal display parameters under different conditions are determined based on these historical data. For example, in a strong light environment, it may be necessary to increase the display brightness to 400 nits, adjust the contrast to 1000:1, and increase the blue light component to improve readability; while in a low light environment, it may be necessary to reduce the brightness to 50 nits, reduce the contrast to 700:1, and reduce the blue light component to protect the eyes. These optimal parameters can be determined through user feedback and professional tests. The data are organized into a training data set, including the input (light intensity and color temperature) and the output (optimal display parameters). For example, a set of data may be: input (100,000 lux, 6000K), output (300 nits, 900:1, color temperature 6500K). By collecting a large number of such data pairs, a comprehensive training set can be formed. Then, the support vector machine (SVM) algorithm is used for modeling training. SVM is suitable for dealing with problems with multi-dimensional input and multi-dimensional output and can effectively learn the non-linear relationship between the input and the output. By adjusting the kernel function (such as the RBF kernel) and other hyperparameters, an accurate mapping model can be obtained. This model can predict the optimal display parameters according to the input light conditions. Finally, the real-time obtained light data are input into the trained model. For example, when the user walks outdoors and the light sensor detects that the light intensity suddenly increases to 80,000 lux and the color temperature becomes 5800K. The model may output a suggestion to adjust the brightness to 350 nits, the contrast to 950:1, and the display color temperature to 6300K. The device can automatically adjust the display parameters according to these suggestions to provide the user with the best visual experience. The advantage of this dynamic adjustment method is that it can adapt to various complex light environments. For example, on a cloudy day outdoors, although the light intensity is not high, the color temperature is high, and at this time, a special combination of display parameters may be required. Another example is at sunset when the light intensity gradually decreases and the color temperature is also changing, and the model can adjust the display parameters in real time to keep the display effect in the best state all the time. In addition, this method can also be customized. By recording the user's manual adjustment behavior, the model can be continuously optimized to make it more in line with personal preferences. For example, if the user often slightly reduces the brightness based on the model's suggestion, the system can learn this preference and appropriately reduce the brightness suggestion in future predictions. For smart glasses, the user's line-of-sight direction can also be considered as an additional input parameter because different line-of-sight directions may face different light environments.Generally speaking, this machine learning-based dynamic display parameter adjustment method can provide the best visual experience for users in various complex and changeable lighting environments, improving the comfort and practicality of smart wearable devices. With the continuous accumulation of data and the continuous optimization of algorithms, the effect of this method will be better and better, bringing a more intelligent and user-friendly experience for users.

[0064] In some embodiments, in the above step S102, the personalized adjustment of the interface element layout of the smart wearable device according to the historical operation information specifically includes:

[0065] Extract the user's usage habit values from the historical operation information, and verify whether the usage habit values conform to the Gaussian distribution. The usage habit values include the area size, area shape, and area position of the interface elements;

[0066] If the usage habit values conform to the Gaussian distribution, determine the distribution law of the interface elements according to the usage habit values, and generate a probability density function f(x, y, z) for characterizing the user's personalized needs, where x represents the area size, y represents the area shape, and z represents the area position;

[0067] Generate a second training dataset using the probability density function f(x, y, z), and use the second training dataset to train a decision tree model to form a user habit model;

[0068] Based on the user habit model, the interface element layout of the smart wearable device is adjusted in real time and personalized.

[0069] In this embodiment, obtain the historical operation information of the user in the application, and extract usage habit values such as the area size, area shape, and area position of the interface elements. Conduct statistical analysis on the extracted usage habit values to determine whether they conform to the Gaussian distribution. If the usage habit values conform to the Gaussian distribution, determine the distribution law of the interface elements in terms of area size, shape, and position according to parameters such as the mean and variance. Use methods such as maximum likelihood estimation to obtain the parameters of the probability density function f(x, y, z) that characterizes the user's personalized needs. When generating a new interface layout, sample the probability density function f(x, y, z) to obtain the area size, shape, and position of the interface elements that conform to the user's usage habits. Use the area size, shape, and position of the sampled interface elements as constraints, and combine other layout rules to generate a personalized interface layout scheme. In actual applications, display the generated personalized interface layout, and continuously collect the user's operation feedback data for optimizing and updating the parameters of the probability density function to achieve continuous personalized adaptation of the interface layout.

[0070] Using the Monte Carlo method, a second training dataset is generated based on the probability density function f(x, y, z). The second training dataset contains a large number of interface element layout samples that conform to the user's usage habits. The second training dataset is input into the decision tree model for training. By adjusting the parameters of the decision tree model, the decision tree model can accurately predict the interface element layout preferred by the user, thereby forming a user habit model. Obtain the current interface element layout of the smart wearable device, input the current layout into the user habit model for prediction, and obtain the interface element layout preferred by the user. Apply the predicted user-preferred layout to the actual interface of the smart wearable device, and perform real-time personalized adjustment on the interface element layout to meet the personalized needs of the user.

[0071] Exemplarily, first, user usage habit values are extracted from historical operation information, including the area size, shape, and position of interface elements. To verify whether these values conform to the Gaussian distribution, statistical methods such as the Shapiro-Wilk test can be used. If they conform to the Gaussian distribution, the distribution law of the interface elements can be determined. For example, it is found that users prefer to operate a circular button with a size of 200 pixels in the central area of the screen. Based on these laws, a probability density function f(x, y, z) is generated, where x represents the area size, y represents the shape, and z represents the position. This function can quantify the user's preference degree for different interface element layouts. Using the probability density function, a second training dataset is generated, which can simulate a large number of interface layout samples that conform to the user's habits. For example, 1000 different interface layout schemes are generated, each containing a combination of elements with different sizes, shapes, and positions. These data are used to train a decision tree model to form a user habit model. The decision tree model can learn complex rules of user preferences, such as "if the button is located in the lower right corner and is circular, then the size should be between 150 - 250 pixels". Based on the trained user habit model, the interface element layout of the smart wearable device can be adjusted in real time. When the user opens a new application, the model will predict the most suitable interface layout for the user. For example, place the frequently used function buttons in a position that is easy for the user to reach, adjust the font size to suit the user's eyesight, or adjust the menu structure according to the user's operation habits. The advantage of this personalized adjustment method is that it can adapt to the dynamic changes of user habits. As the user uses the device for a longer time, their operation habits may change. By continuously collecting new operation data and updating the model, the system can continuously optimize the interface layout and always maintain the best user experience. For example, if it is found that the user starts to use a certain function more frequently, the system will automatically adjust the entry of this function to a more prominent position. In addition, this method can also dynamically adjust the interface according to different scenarios. For example, when it is detected that the user is exercising, the button size can be increased and the interface can be simplified to facilitate the user's operation during exercise. When the user is stationary, more detailed information can be displayed. This intelligent interface adjustment not only improves the user's operation efficiency but also reduces the probability of misoperation, thus significantly enhancing the user experience. Generally speaking, this intelligent interface adjustment method based on user habits realizes the personalization and dynamic optimization of the smart wearable device interface through data-driven and machine learning technologies, providing the user with a more intuitive and convenient operation experience.

[0072] In some embodiments, in the above step S103, the specifically adjusting the second display parameters of each display area of the display content by using an adaptive display algorithm according to the characteristics and importance of the display content of the smart wearable device includes:

[0073] Obtain the texture feature information of the display content of the smart wearable device, and perform data clustering processing on the texture feature information by using the K-means clustering algorithm to obtain feature clustering data;

[0074] Compare the feature clustering data with a preset information category mapping table to determine the business classification probability distribution vector corresponding to all features of the display content;

[0075] Input the business classification probability distribution vector into a pre-constructed logistic regression model, and calculate the importance value of each display area of the display content through the sigmoid function;

[0076] Based on the importance value, dynamically calculate the second display parameters of each display area, where the second display parameters include resolution and refresh rate.

[0077] In this embodiment, obtain the image information of the current display content of the smart wearable device, extract the texture features of the image information to obtain texture feature information. For the texture feature information, perform clustering processing by using the K-means clustering algorithm to obtain multiple clustering centers reflecting different texture features, and obtain feature clustering data. Obtain a preset information category mapping table, where the mapping table records the corresponding relationships between various texture features and business information categories. Compare each clustering center in the feature clustering data with the information category mapping table respectively to determine the business information category corresponding to each clustering center. According to the business information category corresponding to each clustering center and the number of samples included in each clustering center, calculate the probability of each business classification of the display content, and generate a business classification probability distribution vector. Input the probability distribution vector into a pre-constructed logistic regression model, and calculate through the sigmoid function to obtain the importance value of each display area of the display content; according to the importance value, judge the importance degree of each display area. If the importance value is greater than a preset threshold, mark this area as an important area; for different important areas, adopt different display strategies. For important areas, increase their resolution and refresh rate; determine the second display parameters of each display area, including resolution and refresh rate, etc., in a dynamic calculation manner; apply the calculated second display parameters to the content display of each display area to highlight the content of the important areas; during the display process, continuously monitor the change of the importance value of each area, and dynamically adjust the display parameters according to the change to ensure the real-time optimization of the display effect.

[0078] Exemplarily, first of all, the system needs to deeply understand the essence of the display content, which requires extracting the texture feature information of the display content. Texture feature information refers to the features of pixels in an image or display content in terms of color, brightness, spatial distribution, etc., which can reflect the details and structure of the image. For example, the texture features of a face image may include the texture of the skin, the shape of the eyes, the contour of the mouth, etc. Next, in order to effectively process this texture feature information, the system adopts the K-means clustering algorithm. This is a commonly used unsupervised machine learning algorithm that can automatically divide data into several categories. In the K-means algorithm, first, K data points need to be randomly selected as the initial clustering centers, then the distance from each data point to these centers is calculated, and the data points are assigned to the category where the nearest center is located. Then, the center points of each category are recalculated, and the above assignment process is repeated until the category no longer changes or the preset number of iterations is reached. For example, when processing multiple application icons displayed on a smart wearable device, the K-means algorithm can group similar icons into one category according to the texture features such as the color and shape of the icons. For example, all the icons of social applications are grouped into one category, and all the icons of health applications are grouped into another category. After obtaining the feature clustering data, the system compares it with a preset information category mapping table. This mapping table defines the corresponding relationship between different texture features and business categories. For example, delicate texture and bright colors may correspond to the "game" category, while simple lines and single tones may correspond to the "tool" category. By comparison, the business classification probability distribution vector corresponding to all the features of the display content can be determined. For example, after a certain display content undergoes texture feature extraction and clustering, it is found that 70% of its features match the "health monitoring" category, 20% of its features match the "message notification" category, and 10% of its features match the "entertainment" category. Then the corresponding business classification probability distribution vector is (0.7, 0.2, 0.1). In order to further quantify the importance of different regions in the display content, the system inputs the business classification probability distribution vector into a pre-constructed logistic regression model. The logistic regression model is a generalized linear model, commonly used for binary classification problems, but can also be extended to multi-classification scenarios. It maps the output of a linear function to between 0 and 1 through a sigmoid function, representing the probability of belonging to a certain category. Here, the logistic regression model has been trained to predict the importance values of each region of the display content according to the business classification probability distribution vector. For example, the model may predict that the importance value of the region where health monitoring data is located is 0.8, the importance value of the message notification region is 0.6, and the importance value of the entertainment content region is 0.3. Finally, based on these importance values, the system dynamically calculates the second display parameters of each display region, including resolution and refresh rate.For example, for health monitoring areas with a higher importance value, their resolution can be increased to make data such as heart rate and blood oxygen clearer; at the same time, to ensure the real-time nature of the data, the refresh rate of this area can be increased. For entertainment content areas with a lower importance value, the resolution and refresh rate can be appropriately reduced to save power consumption. For example, when the user views stock information, the importance value of the stock chart area may be very high, so the system will prioritize ensuring the resolution and refresh rate of this area. For other areas, such as the time display area, its importance value may be relatively low, so its display parameters can be appropriately reduced. This method of dynamically adjusting display parameters has significant advantages. First of all, it can intelligently allocate display resources according to the user's current focus, improving the readability and real-time nature of key information. Secondly, by reducing the display parameters of unimportant areas, the power consumption of the device can be effectively reduced, extending the battery life. For example, when the user is engaged in long-term outdoor sports, the device can continuously display sports data at a high resolution and high refresh rate, while when the user is resting, the overall display parameters can be reduced to save power. This intelligent display content optimization method, through data-driven and machine learning technologies, realizes the personalization and dynamic adjustment of the display effect of smart wearable devices, providing users with a clearer, smoother, and more energy-efficient visual experience.

[0079] In some embodiments, in the above step S104, the determining whether to perform layout optimization on the display content of the current interaction interface according to the user's eye fatigue degree and the residence time of the current interaction interface specifically includes:

[0080] Collect the pupil image information of the user, and process the pupil image information by using the Hough circle transform algorithm to obtain a pupil diameter sequence;

[0081] Based on the pupil diameter sequence, calculate the average pupil diameter of the user's pupil within each preset time period;

[0082] According to the difference between the average pupil diameters within two adjacent preset time periods in the pupil diameter sequence, determine the pupil diameter change rate, and determine the user's eye fatigue degree through the pupil diameter change rate;

[0083] Obtain the residence time of the user on the current interaction interface of the smart wearable device, and determine whether to perform layout optimization on the display content of the current interaction interface according to the eye fatigue degree and the residence time.

[0084] In this embodiment, the pupil image information of the user is obtained. For the pupil image information, the Hough circle transform algorithm is used for processing to obtain the pupil diameter sequence data. According to the pupil diameter sequence data, the pupil diameter data subset within each preset time period is determined. For each pupil diameter data subset, its average pupil diameter is calculated to obtain the average pupil diameter data within the preset time period. According to the difference between the average pupil diameters in two adjacent preset time periods, the pupil diameter change rate is determined. The pupil diameter change rate is compared with a preset eye fatigue threshold. If the change rate is greater than the threshold, it is determined that the user's eyes are in a fatigued state; otherwise, it is determined that the user's eyes are in a non-fatigued state. The residence time of the user on the current interaction interface of the smart wearable device is obtained and compared with a preset time threshold. If the residence time is greater than the time threshold and the user's eyes are in a fatigued state, it is determined that the display content of the current interaction interface needs to be layout-optimized; otherwise, it is determined that no layout optimization is required. If layout optimization is needed, according to the pre-established layout optimization rules, the display content of the current interaction interface is layout-adjusted to obtain an optimized interface layout. The optimized interface layout is applied to the current interaction interface of the smart wearable device to update the display content to relieve the user's eye fatigue.

[0085] Further, this embodiment further includes: judging whether the average pupil diameter data is greater than a preset pupil diameter threshold. If so, it is determined that the user is in an excited state within the preset time period; otherwise, it is determined that the user is in a calm state. The user state data within a historical preset time period is obtained, and the support vector machine algorithm is used to train the user state data to obtain a user state prediction model. Through the user state prediction model, the state of the user in the next preset time period is predicted, and the predicted user state result is output and displayed. According to the predicted user state result, the reinforcement learning algorithm is used to optimize and update the user state prediction model to improve the accuracy of subsequent user state predictions.

[0086] Exemplarily, the change in pupil diameter can reflect the physiological and psychological states of the human body, and thus becomes an important indicator for evaluating fatigue level. First, the device collects the pupil images of the user. This can be achieved through a built-in miniature camera, such as the camera on smart glasses. The collection frequency can be set to multiple times per second to capture the subtle changes in pupil size. Next, the system processes the pupil images using the Hough circle transform algorithm. The Hough circle transform is an image processing technique for detecting circular objects. In pupil detection, it can effectively identify the boundary of the pupil and calculate the pupil diameter. For example, if the image resolution is 640x480 pixels, the algorithm may identify a circle with a radius of 20 pixels, corresponding to an actual pupil diameter of approximately 4 millimeters. The system calculates the average pupil diameter within a preset time period (such as every 5 minutes). Suppose within a certain 5-minute period, the system collects 300 pupil diameter data, and the average value is 3.8 millimeters. And in the next 5 minutes, the average diameter becomes 3.5 millimeters. The system calculates the change rate of the pupil diameter between these two time periods. The change rate of the pupil diameter is a key indicator for judging eye fatigue. Generally speaking, as the fatigue level increases, the pupil gradually shrinks, and the change rate is negative. For example, if the change rates for three consecutive time periods are -5%, -8%, and -10% respectively, the system may determine that the user is in a moderate fatigue state. In addition to pupil data, the system also records the stay time of the user on the current interaction interface. For example, if the user stays on the reading interface for 30 minutes and the pupil data shows moderate fatigue, the system may decide to optimize the layout of the interface. Such optimization may include increasing the font size, adjusting the contrast, or inserting a rest reminder. This interface optimization method based on physiological data has significant advantages. It can respond in real time to the user's physical state and provide a personalized usage experience. For example, when it detects that the user is fatigued, the system can automatically adjust the display screen brightness or simplify complex information charts to reduce the visual burden. In addition, this method can also help prevent health problems caused by excessive eye use, such as dry eyes, vision decline, etc. In practical applications, the system may combine multiple data to improve accuracy. For example, in addition to pupil diameter, it can also monitor indicators such as blink frequency and fixation point distribution. Through these comprehensive data, the system can more accurately evaluate the user's fatigue state and thus provide a more intelligent and user-friendly interface optimization solution.

[0087] Further, after determining whether to optimize the layout of the display content of the current interaction interface according to the eye fatigue level and the stay time, it further includes:

[0088] If it is determined to optimize the layout of the display content of the current interaction interface, the priority parameters of each display area of the display content of the current interaction interface are determined according to the historical interaction information of the user on the current interaction interface;

[0089] According to the priority parameters, the layout of the display content of the current interaction interface is readjusted.

[0090] In this embodiment, historical interaction information of the user on the current interaction interface is obtained, including data such as clicks, browsing, and residence time. The historical interaction data of the user is analyzed to mine the user's interaction behavior patterns and preference habits. According to the results of the interaction data analysis, the priority parameters of each display area of the current interaction interface are determined. The priority parameters are combined with the preset interface design rules, and a dynamic layout algorithm is used to adjust the layout of the display content of the current interaction interface. During the layout adjustment process, the display areas with high priority are placed in prominent positions on the interface to increase their exposure. At the same time, according to the user's interaction preferences, the content within the display area is personalized arranged and recommended. Finally, an optimized interaction interface layout scheme is generated, and the user's behavior is continuously tracked during the subsequent interaction process of the user, and the interface layout is dynamically adjusted in real time to adapt to the changing interaction needs of the user.

[0091] Exemplarily, first, the system analyzes the historical interaction information of the user on the current interaction interface, which may include the user's click heatmap, dwell time distribution, sliding trajectory, etc. For example, on the main interface of a health monitoring application, the system may find that the user often focuses on the heart rate data area and seldom views the sleep quality report. Based on this historical data, the system assigns priority parameters to each display area of the interface. The priority parameter can be a value between 0 and 1, and the higher the value, the more important the area. For example, the heart rate data area may be assigned a priority of 0.9, while the sleep quality report area may only have a priority of 0.3. After determining the priority parameters, the system readjusts the interface layout according to these parameters. Areas with high priority may be enlarged or moved to a more prominent position, while areas with low priority may be reduced or moved to a secondary position. For example, the heart rate data area may be enlarged and moved to the top of the screen, while the sleep quality report may be reduced and moved to the bottom. This dynamic adjustment is not limited to the size and position of the area, but may also involve the presentation method of the content. For high-priority information, the system may use more eye-catching colors, larger fonts, or add animation effects to attract the user's attention. For example, the heart rate data may be presented as a beating heart icon instead of static numbers. In addition, the system may also adjust the arrangement of function buttons according to the user's usage habits. If it is found that the user frequently uses a certain function, the corresponding button may be moved to a more accessible position. For example, if the user often uses the sports mode, the shortcut button for this function may be moved to the quick access bar at the bottom of the interface. This priority-based layout optimization method has many advantages. First, it can improve the user's operation efficiency and allow the user to find the required information faster. Second, it can reduce the user's visual burden, especially on small-screen devices, by highlighting important information to reduce the risk of information overload. Finally, this method can adapt to changes in user needs, dynamically adjust as the user's usage habits change, and provide a continuously optimized user experience. In practical applications, the system may combine multiple factors to determine the priority parameters. In addition to historical interaction information, context information such as time and location may also be considered. For example, when waking up in the morning, the priority of the sleep quality report may be temporarily increased. Another example is that when the user is in the gym, the priority of sports-related functions may be automatically increased. Through this intelligent interface optimization method, the smart wearable device can provide a customized usage experience for each user, not only improving the practicality of the device, but also enhancing user stickiness and satisfaction.

[0092] In some embodiments, in the above step S105, the training of the attention mechanism model according to the historical fixation information and the determination of the loading priorities of the respective display areas of the smart wearable device by the trained attention mechanism model specifically include:

[0093] Preprocess the historical gaze information to extract gaze feature data;

[0094] Based on the gaze feature data, use a recurrent neural network to train an attention mechanism model to obtain a display area loading priority allocation model;

[0095] Based on the display area loading priority allocation model, calculate the attention weights of each display area of the smart wearable device, and sort the loading priorities of each display area according to the attention weights to form a loading priority list;

[0096] Based on the loading priority list, dynamically adjust the information loading progress of each display area according to the network condition and device performance of the smart wearable device.

[0097] In this embodiment, obtain the historical gaze information of the user, perform denoising and normalization processing on the historical gaze information to obtain the preprocessed gaze information. Extract gaze features from the preprocessed gaze information, including gaze point coordinates, gaze duration, gaze trajectory, etc., and construct gaze feature data. According to the constructed gaze feature data, use a long short-term memory network to train an attention mechanism model to obtain a preliminary attention model. For the preliminary attention model obtained by training, introduce a gaze heat weight to perform weighted processing on the gaze feature data and update the weight parameters of the attention model. Based on the updated attention model, combined with the layout information of the display area, calculate the loading priority probability distribution of each display area through the softmax function. According to the calculated loading priority probability distribution, construct a display area loading priority allocation model to realize the priority sorting of the display area.

[0098] Obtain the layout information and content information of multiple display areas of the smart wearable device, extract the feature vectors of each display area according to the display area loading priority allocation model. Calculate the attention weights of each display area using the attention mechanism based on the feature vectors of each display area, and obtain the weight distribution reflecting the importance of the display area. Sort the loading priorities of each display area according to the attention weights to generate a display area loading priority list, and the display area with a higher priority is loaded first. Obtain the current network status parameters and device performance parameters of the smart wearable device, and judge whether the current network status and device performance meet the loading requirements of the display area content. If the network status and device performance meet the requirements, load the information content of each display area in parallel according to the order of the display areas in the loading priority list. If the network status and device performance do not meet the requirements, adjust the loading progress of each display area according to the loading priority list to give priority to ensuring the loading quality of the high-priority display area. Dynamically track the changes in the network status and device performance of the smart wearable device, and adjust the information loading progress of the display area in real time according to the changes to ensure the fluency and timeliness of information loading.

[0099] Exemplarily, during the interface optimization of a smart wearable device, the historical fixation information is first preprocessed. This step involves cleaning the raw data, removing outliers, and extracting meaningful features. For example, metrics such as the fixation duration and fixation frequency of the user on each interface element can be calculated. The processed data constitutes the fixation feature data, providing a basis for subsequent model training. Based on the fixation feature data, a recurrent neural network (RNN) is used to train an attention mechanism model. The RNN is chosen because it can effectively process sequential data and capture the temporal dependence of the user's fixation behavior. In this process, variants such as long short-term memory network (LSTM) or gated recurrent unit (GRU) can be used to better handle long-term dependencies. During the training process, the model learns to map the fixation sequence to the importance weights of each display area, thereby forming a display area loading priority allocation model. Using the trained model, the attention weights can be calculated for each display area of the smart wearable device. For example, in a health monitoring application, the heart rate monitoring area may obtain a weight of 0.8, while the weather information area may only have a weight of 0.3. These weights reflect the user's attention to different information, providing a quantitative basis for interface optimization. According to the calculated attention weights, the system sorts the loading priorities of the display areas to form a loading priority list. This list determines which information should be loaded and displayed first when resources are limited. For example, high-priority heart rate data may be arranged at the top of the list to ensure the fastest loading and update. Finally, based on the loading priority list and combined with the current network condition and performance of the device, the system dynamically adjusts the information loading progress of each display area. This dynamic adjustment mechanism can prioritize the timely update of important information when the network condition is poor. For example, in the case of limited network bandwidth, the system may reduce the refresh frequency or display quality of low-priority areas to ensure the smooth loading of high-priority areas. This dynamic loading strategy based on the attention mechanism not only improves the user experience but also optimizes the utilization efficiency of device resources. By preferentially loading the information that the user cares most about, unnecessary data transmission and processing are reduced, thereby extending the battery life and improving the overall performance of the device. At the same time, the adaptability of this method enables the device to continuously adjust according to changes in user habits, providing a continuously optimized personalized experience.

[0100] Referring to Figure 2 , an embodiment of the present invention provides a display system 2 for a smart wearable device. The system 2 specifically includes:

[0101] A first display module 201, configured to detect in real time the light intensity data and color temperature data of the usage environment of the smart wearable device, and dynamically adjust the first display parameters of the smart wearable device according to the light intensity data and the color temperature data. The first display parameters include display brightness, display contrast, and display color.

[0102] A second display module 202, configured to obtain historical operation information of a user on interface elements of the smart wearable device, and perform personalized adjustment on the layout of the interface elements of the smart wearable device according to the historical operation information;

[0103] A third display module 203, configured to dynamically adjust second display parameters of each display area of the display content according to characteristics and importance of the display content of the smart wearable device by using an adaptive display algorithm, where the second display parameters include resolution and refresh rate;

[0104] A fourth display module 204, configured to determine whether to perform layout optimization on the display content of the current interaction interface according to the eye fatigue degree of the user and the residence time of the current interaction interface;

[0105] A fifth display module 205, configured to obtain historical fixation information when the user operates the smart wearable device, train an attention mechanism model according to the historical fixation information, and determine a loading priority of each display area of the smart wearable device through the trained attention mechanism model.

[0106] It can be understood that the content in the embodiment of the display method of the smart wearable device as Figure 1 shown is applicable to the embodiment of the display system of the present smart wearable device. The functions specifically implemented by the embodiment of the display system of the present smart wearable device are the same as those in the embodiment of the display method of the smart wearable device as Figure 1 shown, and the beneficial effects achieved are also the same as those achieved by the embodiment of the display method of the smart wearable device as Figure 1 shown.

[0107] It should be noted that the information interaction, execution process, etc. between the above systems, due to being based on the same concept as the method embodiment of the present invention, for their specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details are not described herein again.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be elaborated herein.

[0109] Referring to Figure 3 , an embodiment of the present invention further provides a computer device 3, including: a memory 302, a processor 301, and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, it implements the display method of the smart wearable device as described in any one of the above methods.

[0110] The computer device 3 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art can understand that Figure 3 this is only an example of the computer device 3 and does not constitute a limitation on the computer device 3. It may include more or fewer components than shown in the figure, or combine some components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0111] The so-called processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0112] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as the hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device 3. Further, the memory 302 may also include both the internal storage unit and the external storage device of the computer device 3. The memory 302 is used to store an operating system, application programs, a Boot Loader, data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0113] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the display method of the smart wearable device as described in any one of the above methods is implemented.

[0114] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above embodiment methods of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium may at least include: any entity or device capable of carrying the computer program code to the photographing device / terminal device, a recording medium, a computer memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium may not be an electrical carrier signal and a telecommunication signal.

[0115] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0116] Those of ordinary skill in the art will appreciate that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0117] In the embodiments disclosed in this application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A display method for a smart wearable device, characterized in that, The method specifically includes: Real-time detecting the light intensity data and color temperature data of the usage environment of the smart wearable device, and dynamically adjusting the first display parameters of the smart wearable device according to the light intensity data and the color temperature data, where the first display parameters include display brightness, display contrast, and display color; Obtaining the historical operation information of the user on the interface elements of the smart wearable device, and performing personalized adjustment on the layout of the interface elements of the smart wearable device according to the historical operation information; According to the characteristics and importance of the display content of the smart wearable device, dynamically adjusting the second display parameters of each display area of the display content by using an adaptive display algorithm, where the second display parameters include resolution and refresh rate; Determining whether to optimize the layout of the display content of the current interaction interface according to the user's eye fatigue degree and the residence time of the current interaction interface; Obtaining the historical gaze information when the user operates the smart wearable device, training the attention mechanism model according to the historical gaze information, and determining the loading priority of each display area of the smart wearable device through the trained attention mechanism model.

2. The method according to claim 1, wherein The dynamically adjusting the first display parameters of the smart wearable device according to the light intensity data and the color temperature data specifically includes: Obtaining historical light intensity data and historical color temperature data, determining the optimal display brightness, display contrast, and optimal display color of the smart wearable device under different light intensity conditions and different color temperature conditions according to the historical light intensity data and the historical color temperature data, and forming a first training data set; Taking the first training data set as input, performing modeling training by using a support vector machine algorithm to form a first display parameter mapping model; Inputting the light intensity data and the color temperature data into the first display parameter mapping model for matching, obtaining a matching result, and dynamically adjusting the first display parameters of the smart wearable device according to the matching result.

3. The method according to claim 1, wherein The performing personalized adjustment on the layout of the interface elements of the smart wearable device according to the historical operation information specifically includes: Extracting the usage habit values of the user from the historical operation information, and verifying whether the usage habit values conform to a Gaussian distribution, where the usage habit values include the area size, area shape, and area position of the interface elements; If the usage habit values conform to a Gaussian distribution, determining the distribution rule of the interface elements according to the usage habit values, and generating a probability density function f(x, y, z) for characterizing the user's personalized needs, where x represents the area size, y represents the area shape, and z represents the area position; Generating a second training data set by using the probability density function f(x, y, z), and training a decision tree model by using the second training data set to form a user habit model; Based on the user habit model, performing personalized adjustment on the layout of the interface elements of the smart wearable device in real time.

4. The method according to claim 1, characterized in that, The dynamically adjusting the second display parameters of each display area of the display content by using an adaptive display algorithm according to the characteristics and importance of the display content of the smart wearable device specifically includes: Obtain the texture feature information of the display content of the smart wearable device, and use the K-means clustering algorithm to perform data clustering processing on the texture feature information to obtain feature clustering data; Compare the feature clustering data with a preset information category mapping table to determine the business classification probability distribution vector corresponding to all features of the display content; Input the business classification probability distribution vector into a pre-constructed logistic regression model, and calculate the importance value of each display area of the display content through the sigmoid function; Based on the importance value, dynamically calculate the second display parameters of each display area, and the second display parameters include resolution and refresh rate.

5. The method according to claim 1, wherein Determine whether to optimize the layout of the display content of the current interaction interface according to the user's eye fatigue degree and the residence time of the current interaction interface, specifically including: Collect the pupil image information of the user, and use the Hough circle transformation algorithm to process the pupil image information to obtain a pupil diameter sequence; Based on the pupil diameter sequence, calculate the average pupil diameter of the user's pupil in each preset time period; According to the difference between the average pupil diameters in two adjacent preset time periods in the pupil diameter sequence, determine the pupil diameter change rate, and determine the user's eye fatigue degree through the pupil diameter change rate; Obtain the residence time of the user on the current interaction interface of the smart wearable device, and determine whether to optimize the layout of the display content of the current interaction interface according to the eye fatigue degree and the residence time.

6. The method according to claim 5, wherein After determining whether to optimize the layout of the display content of the current interaction interface according to the eye fatigue degree and the residence time, it further includes: If it is determined to optimize the layout of the display content of the current interaction interface, determine the priority parameter of each display area of the display content of the current interaction interface according to the historical interaction information of the user on the current interaction interface; According to the priority parameter, re-adjust the layout of the display content of the current interaction interface.

7. The method according to claim 1, wherein Train the attention mechanism model according to the historical fixation information, and determine the loading priority of each display area of the smart wearable device through the trained attention mechanism model, specifically including: Preprocess the historical fixation information and extract fixation feature data; Based on the fixation feature data, use a recurrent neural network to train the attention mechanism model to obtain a display area loading priority allocation model; Based on the display area loading priority allocation model, calculate the attention weights of each display area of the smart wearable device, and sort the loading priorities of each display area according to the attention weights to form a loading priority list; Based on the loading priority list, dynamically adjust the information loading progress of each display area according to the network status and device performance of the smart wearable device.

8. A display system of an intelligent wearable device, characterized in that, The system specifically includes: The first display module is configured to detect in real time the light intensity data and color temperature data of the usage environment of the smart wearable device, and dynamically adjust the first display parameters of the smart wearable device according to the light intensity data and the color temperature data, where the first display parameters include display brightness, display contrast, and display color; The second display module is configured to obtain the historical operation information of the user on the interface elements of the smart wearable device, and perform personalized adjustment on the layout of the interface elements of the smart wearable device according to the historical operation information; The third display module is configured to dynamically adjust the second display parameters of each display area of the display content by using an adaptive display algorithm according to the characteristics and importance of the display content of the smart wearable device, where the second display parameters include resolution and refresh rate; The fourth display module is configured to determine whether to optimize the layout of the display content of the current interaction interface according to the user's eye fatigue degree and the residence time of the current interaction interface; The fifth display module is configured to obtain the historical gaze information of the user when operating the smart wearable device, train an attention mechanism model according to the historical gaze information, and determine the loading priority of each display area of the smart wearable device through the trained attention mechanism model.

9. A computer device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory, where when the computer program is executed on the processor, the display method of the smart wearable device as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is run by the processor, the display method of the smart wearable device as described in any one of claims 1 to 7 is implemented.

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